# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# ! pip install wordcloud
# ! pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_ott = df_movies.copy()
df_movies_ott.drop(df_movies_ott.loc[df_movies_ott['Title'] == "NA"].index, inplace = True)
# df_movies_ott = df_movies_ott[df_movies_ott.Title != "NA"]
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_ott_movies = df_movies_ott.loc[df_movies_ott['Netflix'] == 1]
hulu_ott_movies = df_movies_ott.loc[df_movies_ott['Hulu'] == 1]
prime_video_ott_movies = df_movies_ott.loc[df_movies_ott['Prime Video'] == 1]
disney_ott_movies = df_movies_ott.loc[df_movies_ott['Disney+'] == 1]
df_movies_ott_group = df_movies_ott.copy()
plt.figure(figsize = (10, 10))
corr = df_movies_ott.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
print('\nMovies Available on All Platfroms Are : \n')
df_movies_ott.head(5)
Movies Available on All Platfroms Are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
print('\nMovies Available on Netflix Are : \n')
netflix_ott_movies.head(5)
Movies Available on Netflix Are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
print('\nMovies Available on Hulu Are : \n')
hulu_ott_movies.head(5)
Movies Available on Hulu Are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66 | 67 | Blackfish | 2013 | 13 | 8.1 | 98 | Gabriela Cowperthwaite | Tilikum,John Hargrove,Samantha Berg,Mark Simmo... | Documentary | United States | English,Spanish | Notorious killer whale Tilikum is responsible ... | 83 | movie | 1 | 1 | 0 | 0 | 0 | Netflix |
| 94 | 95 | Jiro Dreams of Sushi | 2011 | 7 | 7.9 | 99 | David Gelb | Jiro Ono,Yoshikazu Ono,Masuhiro Yamamoto,Daisu... | Documentary | United States | Japanese | In the basement of a Tokyo office building, 85... | 81 | movie | 1 | 1 | 0 | 0 | 0 | Netflix |
| 142 | 143 | The Patriot | 2000 | 16 | 7.2 | 62 | Roland Emmerich | Mel Gibson,Heath Ledger,Joely Richardson,Jason... | Action,Drama,History,War | United States,Germany | English,French | It is 1776 in colonial South Carolina. Benjami... | 165 | movie | 1 | 1 | 0 | 0 | 0 | Netflix |
| 144 | 145 | The Square | 2013 | 16 | 7.2 | 20 | Ruben Östlund | Claes Bang,Elisabeth Moss,Dominic West,Terry N... | Comedy,Drama | Sweden,Germany,France,Denmark,United States | Swedish,English,Danish | Christian is the respected curator of a contem... | 151 | movie | 1 | 1 | 1 | 0 | 0 | Netflix |
| 210 | 211 | Hitch | 2005 | 13 | 6.6 | 69 | Andy Tennant | Will Smith,Eva Mendes,Kevin James,Amber Vallet... | Comedy,Romance | United States | English | Pivoting around the eternal game of love, the ... | 118 | movie | 1 | 1 | 0 | 0 | 0 | Netflix |
print('\nMovies Available on Prime Video Are : \n')
prime_video_ott_movies.head(5)
Movies Available on Prime Video Are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
| 6 | 7 | The Pianist | 2002 | 16 | 8.5 | 95 | Roman Polanski | Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... | Biography,Drama,Music,War | United Kingdom,France,Poland,Germany,United St... | English,German,Russian | In this adaptation of the autobiography "The P... | 150 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
| 11 | 12 | 3 Idiots | 2009 | 13 | 8.4 | 100 | Rajkumar Hirani | Aamir Khan,Madhavan,Sharman Joshi,Kareena Kapo... | Comedy,Drama | India | Hindi,English | Farhan Qureshi and Raju Rastogi want to re-uni... | 170 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
| 15 | 16 | Once Upon a Time in the West | 1968 | 13 | 8.5 | 95 | Sergio Leone | Claudia Cardinale,Henry Fonda,Jason Robards,Ch... | Western | Italy,United States | English,Italian,Spanish | Jill McBain travels to the wild frontier; Utah... | 165 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
| 31 | 32 | Drive | 2011 | 16 | 7.8 | 38 | Nicolas Winding Refn | Ryan Gosling,Carey Mulligan,Bryan Cranston,Alb... | Crime,Drama | United States | English,Spanish | This action drama follows a mysterious man who... | 100 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
print('\nMovies Available on Disney+ Are : \n')
disney_ott_movies.head(5)
Movies Available on Disney+ Are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 92 | 93 | Saving Mr. Banks | 2013 | 13 | 7.5 | 79 | John Lee Hancock | Emma Thompson,Tom Hanks,Annie Rose Buckley,Col... | Biography,Comedy,Drama | United States,United Kingdom,Australia | English | When Walter Elias Disney's (Tom Hanks') daught... | 125 | movie | 1 | 0 | 0 | 1 | 0 | Netflix |
| 118 | 119 | Bolt | 2008 | 7 | 6.8 | 89 | Byron Howard,Chris Williams | John Travolta,Miley Cyrus,Susie Essman,Mark Wa... | Animation,Adventure,Comedy,Family,Fantasy | United States | English | Bolt, an American white shepherd, has lived hi... | 96 | movie | 1 | 0 | 0 | 1 | 0 | Netflix |
| 121 | 122 | The Princess and the Frog | 2009 | 0 | 7.1 | 85 | Ron Clements,John Musker | Anika Noni Rose,Bruno Campos,Keith David,Micha... | Animation,Adventure,Comedy,Family,Fantasy,Musi... | United States | English,French | A modern day retelling of the classic story Th... | 97 | movie | 1 | 0 | 0 | 1 | 0 | Netflix |
| 146 | 147 | Miracle | 2004 | 7 | 7.5 | 81 | Gavin O'Connor | Kurt Russell,Patricia Clarkson,Noah Emmerich,S... | Biography,Drama,History,Sport | Canada,United States | English | The inspiring story of the team that transcend... | 135 | movie | 1 | 0 | 0 | 1 | 0 | Netflix |
| 474 | 475 | White Fang | 2018 | 7 | 6.7 | 65 | Randal Kleiser | Jed,Klaus Maria Brandauer,Ethan Hawke,Seymour ... | Adventure,Drama | United States | English | Jack London's immortal tale of courage and sur... | 107 | movie | 1 | 0 | 0 | 1 | 0 | Netflix |
print(f'''
Total '{df_movies_ott['Title'].count()}' Titles are available on All Platforms, out of Which,\n
Total '{netflix_ott_movies['Title'].count()}' Titles are available on 'Netflix'
Total '{hulu_ott_movies['Title'].count()}' Titles are available on 'Hulu'
Total '{prime_video_ott_movies['Title'].count()}' Titles are available on 'Prime video'
Total '{disney_ott_movies['Title'].count()}' Titles are available on 'Disney+'
''')
Total '16923' Titles are available on All Platforms, out of Which,
Total '3637' Titles are available on 'Netflix'
Total '1060' Titles are available on 'Hulu'
Total '12307' Titles are available on 'Prime video'
Total '561' Titles are available on 'Disney+'
Platform = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
Count = [netflix_ott_movies['Title'].count(),
hulu_ott_movies['Title'].count(),
prime_video_ott_movies['Title'].count(),
disney_ott_movies['Title'].count()]
fig = px.pie(names = Platform,
values = Count,
title = 'Movies Count Of Different Platforms',
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside',
textinfo = 'percent + label')
fig.show()
df_movies_ott['OTT Count'] = df_movies_ott['Netflix'] + df_movies_ott['Hulu'] + df_movies_ott['Prime Video'] + df_movies_ott['Disney+']
(df_movies_ott['OTT Count'].value_counts()/df_movies_ott.shape[0])*100
1 96.259528 2 3.687289 3 0.053182 Name: OTT Count, dtype: float64
print(f'''
Total '{df_movies_ott[df_movies_ott['OTT Count'] == 4].shape[0]}' Titles are available on All Platforms
Total '{df_movies_ott[df_movies_ott['OTT Count'] == 3].shape[0]}' Titles are available on at least 3 Platforms
Total '{df_movies_ott[df_movies_ott['OTT Count'] == 2].shape[0]}' Titles are available on at least 2 Platforms
Total '{df_movies_ott[df_movies_ott['OTT Count'] == 1].shape[0]}' Titles are available on at least 1 Platforms
''')
Total '0' Titles are available on All Platforms
Total '9' Titles are available on at least 3 Platforms
Total '624' Titles are available on at least 2 Platforms
Total '16290' Titles are available on at least 1 Platforms
# Movies Available on All Platforms
# df_movies_ott[(df_movies_ott['Netflix'] == 1) & (df_movies_ott['Hulu'] == 1) & (df_movies_ott['Prime Video'] == 1) & (df_movies_ott['Disney+'] == 1)]
print('\nTotal ', df_movies_ott[df_movies_ott['OTT Count'] == 4].shape[0], ' Titles are available on All Platforms\n')
movies_on_4_platforms = df_movies_ott[df_movies_ott['OTT Count'] == 4]
movies_on_4_platforms
Total 0 Titles are available on All Platforms
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count |
|---|
0 rows × 21 columns
# Movies Available on at least 3 Platforms
print('\nTotal ', df_movies_ott[df_movies_ott['OTT Count'] == 3].shape[0], ' Titles are available on at least 3 Platforms\n')
movies_on_3_platforms = df_movies_ott[df_movies_ott['OTT Count'] == 3]
movies_on_3_platforms
Total 9 Titles are available on at least 3 Platforms
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 144 | 145 | The Square | 2013 | 16 | 7.2 | 20 | Ruben Östlund | Claes Bang,Elisabeth Moss,Dominic West,Terry N... | Comedy,Drama | Sweden,Germany,France,Denmark,United States | ... | Christian is the respected curator of a contem... | 151 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 334 | 335 | The Interview | 2014 | 16 | 6.5 | 63 | Evan Goldberg,Seth Rogen | James Franco,Seth Rogen,Lizzy Caplan,Randall P... | Action,Adventure,Comedy | United States | ... | In the action-comedy The Interview, Dave Skyla... | 112 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 489 | 490 | Blame! | 2017 | 13 | 6.7 | 93 | Hiroyuki Seshita | Takahiro Sakurai,Kana Hanazawa,Sora Amamiya,Ma... | Animation,Action,Drama,Sci-Fi,Thriller | Japan | ... | In the distant technological future, civilizat... | 106 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 601 | 602 | Evolution | 2001 | 13 | 6.1 | 17 | Ivan Reitman | David Duchovny,Julianne Moore,Orlando Jones,Se... | Comedy,Sci-Fi | United States | ... | When a meteorite falls to Earth two college pr... | 101 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 1113 | 1114 | No Game No Life: Zero | 2017 | 13 | 7.5 | NA | Atsuko Ishizuka | Alexandra Bedford,Jessica Boone,Ricardo Contre... | Animation,Adventure,Comedy,Drama,Fantasy,Romance | Japan | ... | The Movie following the light novel series by ... | 110 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 1982 | 1983 | Mother | 2016 | 16 | 6.6 | 100 | Darren Aronofsky | Jennifer Lawrence,Javier Bardem,Ed Harris,Mich... | Drama,Horror,Mystery | United States | ... | Amidst a wild flat meadow encircled by an Eden... | 121 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
| 3851 | 3852 | The Kid | 2019 | 16 | 5.9 | 87 | Vincent D'Onofrio | Jake Schur,Leila George,Chris Pratt,Dane DeHaa... | Biography,Drama,Western | United States | ... | New Mexico Territory, 1880. Rio Cutler and his... | 100 | movie | 0 | 1 | 1 | 1 | 0 | Prime Video | 3 |
| 4192 | 4193 | Inside Out | 2011 | 7 | 8.1 | 98 | Pete Docter,Ronnie Del Carmen | Amy Poehler,Phyllis Smith,Richard Kind,Bill Ha... | Animation,Adventure,Comedy,Drama,Family,Fantasy | United States | ... | Growing up can be a bumpy road, and it's no ex... | 95 | movie | 0 | 1 | 1 | 1 | 0 | Prime Video | 3 |
| 16246 | 16247 | Rurouni Kenshin | 1996 | 18 | 7.5 | NA | Keishi Ohtomo | Takeru Satoh,Emi Takei,Yû Aoi,Munetaka Aoki,Gô... | Action,Adventure,Drama,History | Japan | ... | NA | 134 | movie | 1 | 1 | 1 | 0 | 0 | Netflix | 3 |
9 rows × 21 columns
# Movies Available on at least 2 Platforms
print('\nTotal ', df_movies_ott[df_movies_ott['OTT Count'] == 2].shape[0], ' Titles are available on at least 2 Platforms\n')
movies_on_2_platforms = df_movies_ott[df_movies_ott['OTT Count'] == 2]
movies_on_2_platforms
Total 624 Titles are available on at least 2 Platforms
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | ... | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 2 |
| 6 | 7 | The Pianist | 2002 | 16 | 8.5 | 95 | Roman Polanski | Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... | Biography,Drama,Music,War | United Kingdom,France,Poland,Germany,United St... | ... | In this adaptation of the autobiography "The P... | 150 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 2 |
| 11 | 12 | 3 Idiots | 2009 | 13 | 8.4 | 100 | Rajkumar Hirani | Aamir Khan,Madhavan,Sharman Joshi,Kareena Kapo... | Comedy,Drama | India | ... | Farhan Qureshi and Raju Rastogi want to re-uni... | 170 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 2 |
| 15 | 16 | Once Upon a Time in the West | 1968 | 13 | 8.5 | 95 | Sergio Leone | Claudia Cardinale,Henry Fonda,Jason Robards,Ch... | Western | Italy,United States | ... | Jill McBain travels to the wild frontier; Utah... | 165 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 2 |
| 31 | 32 | Drive | 2011 | 16 | 7.8 | 38 | Nicolas Winding Refn | Ryan Gosling,Carey Mulligan,Bryan Cranston,Alb... | Crime,Drama | United States | ... | This action drama follows a mysterious man who... | 100 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16555 | 16556 | It's Alive with Brad | 2018 | NR | NA | NA | NA | Shep Gordon | Short | United States | ... | NA | 12 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 2 |
| 16564 | 16565 | #ThatsHarassment | 2018 | 18 | 6.1 | NA | Sigal Avin | Emmy Rossum,Harry Lennix,David Schwimmer,Zazie... | Short | United States | ... | NA | 26 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 2 |
| 16567 | 16568 | Actually Me | 2017 | 16 | 7.6 | NA | Richard Curtis | Bill Nighy,Gregor Fisher,Rory MacGregor,Colin ... | Comedy,Drama,Romance | United Kingdom,United States,France | ... | NA | 135 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 2 |
| 16572 | 16573 | Hello World | 2016 | NR | 6.8 | NA | Tomohiko Itô | Haruka Fukuhara,Minami Hamabe,Takumi Kitamura,... | Animation,Comedy,Drama,Family,Romance,Sci-Fi | Japan | ... | NA | 97 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 2 |
| 16573 | 16574 | You Sang My Song | 2017 | NR | 6.8 | NA | Walter Lang | Susan Hayward,Rory Calhoun,David Wayne,Thelma ... | Biography,Drama,Musical | United States | ... | NA | 117 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 2 |
624 rows × 21 columns
# Movies Available on at least 1 Platform
print('\nTotal ', df_movies_ott[df_movies_ott['OTT Count'] == 1].shape[0], ' Titles are available on at least 1 Platforms\n')
movies_on_1_platforms = df_movies_ott[df_movies_ott['OTT Count'] == 1]
movies_on_1_platforms
Total 16290 Titles are available on at least 1 Platforms
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | ... | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | ... | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | ... | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | ... | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 5 | 6 | Spider-Man: Into the Spider-Verse | 2018 | 7 | 8.4 | 97 | Bob Persichetti,Peter Ramsey,Rodney Rothman | Shameik Moore,Jake Johnson,Hailee Steinfeld,Ma... | Animation,Action,Adventure,Family,Sci-Fi | United States | ... | Phil Lord and Christopher Miller, the creative... | 117 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16918 | 16919 | Pick of the Litter | 2019 | 7 | 7.5 | NA | Don Hardy,Dana Nachman | Diane Meer,Terry Blosser,Janet Gearheart,Sharo... | Documentary | United States | ... | Join National Geographic Explorer and photogra... | 80 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16919 | 16920 | The Lodge | 2016 | 16 | 6.1 | NA | Severin Fiala,Veronika Franz | Riley Keough,Jaeden Martell,Lia McHugh,Richard... | Drama,Horror,Thriller | United Kingdom,United States,Canada | ... | NA | 108 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16920 | 16921 | Spin and Marty | 1955 | 0 | 7.6 | NA | NA | Tim Considine,David Stollery,Roy Barcroft,Harr... | Family,Western | United States | ... | Each inspiring episode of Dog: Impossible foll... | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16921 | 16922 | Teacher's Pet | 2000 | 0 | 7.1 | NA | George Seaton | Clark Gable,Doris Day,Gig Young,Mamie Van Dore... | Comedy,Romance | United States | ... | NA | 120 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16922 | 16923 | Paradise Islands | 2017 | 13 | NA | NA | NA | NA | Drama | United States | ... | NA | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
16290 rows × 21 columns
df_netflix_only_movies_ott = df_movies_ott[(df_movies_ott['Netflix'] == 1) & (df_movies_ott['Hulu'] == 0) & (df_movies_ott['Prime Video'] == 0 ) & (df_movies_ott['Disney+'] == 0)]
df_hulu_only_movies_ott = df_movies_ott[(df_movies_ott['Netflix'] == 0) & (df_movies_ott['Hulu'] == 1) & (df_movies_ott['Prime Video'] == 0 ) & (df_movies_ott['Disney+'] == 0)]
df_prime_video_only_movies_ott = df_movies_ott[(df_movies_ott['Netflix'] == 0) & (df_movies_ott['Hulu'] == 0) & (df_movies_ott['Prime Video'] == 1 ) & (df_movies_ott['Disney+'] == 0)]
df_disney_only_movies_ott = df_movies_ott[(df_movies_ott['Netflix'] == 0) & (df_movies_ott['Hulu'] == 0) & (df_movies_ott['Prime Video'] == 0 ) & (df_movies_ott['Disney+'] == 1)]
# Movies Available only on Netflix
print('\nTotal ', df_netflix_only_movies_ott['Title'].shape[0], ' Titles are available only on Netflix\n')
df_netflix_only_movies_ott
Total 3268 Titles are available only on Netflix
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | ... | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | ... | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | ... | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | ... | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 5 | 6 | Spider-Man: Into the Spider-Verse | 2018 | 7 | 8.4 | 97 | Bob Persichetti,Peter Ramsey,Rodney Rothman | Shameik Moore,Jake Johnson,Hailee Steinfeld,Ma... | Animation,Action,Adventure,Family,Sci-Fi | United States | ... | Phil Lord and Christopher Miller, the creative... | 117 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16413 | 16414 | Lovely Love Lie (The Liar and His Lover) | 2017 | NR | 7 | NA | Norihiro Koizumi | Takeru Satoh,Masataka Kubota,Yûki Morinaga,Ryô... | Romance | Japan | ... | In direct opposition to John Evans' wishes, hi... | 117 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 16414 | 16415 | Truth | 2012 | 16 | 6.8 | NA | James Vanderbilt | Cate Blanchett,Robert Redford,Topher Grace,Den... | Biography,Drama,History,Thriller | Australia,United States | ... | Welcome to Jingle City. It is a peaceful fairy... | 125 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 16415 | 16416 | In Between | 2012 | NR | 7.3 | NA | Maysaloun Hamoud | Mouna Hawa,Sana Jammelieh,Shaden Kanboura,Mahm... | Drama | France,Israel | ... | Sin Senos No Hay Paraiso tells the tragic stor... | 103 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 16416 | 16417 | Fairground Attractions | 2011 | NR | 5.7 | NA | Mehmet Eryilmaz | Fatih Al,Erol Babaoglu,Ahu Sila Bayer,Alpaslan... | Drama | Turkey | ... | NA | 113 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
| 16417 | 16418 | The Golden Path | 2012 | NR | NA | NA | Maurice Costello,Robert Gaillard | Maurice Costello,Mary Charleson,Naomi Childers... | Short,Drama | United States | ... | Captain Ray Holt takes over Brooklyn's 99th pr... | 23 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 1 |
3268 rows × 21 columns
# Movies Available only on Hulu
print('\nTotal ', df_hulu_only_movies_ott['Title'].shape[0], ' Titles are available only on Hulu\n')
df_hulu_only_movies_ott
Total 783 Titles are available only on Hulu
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3459 | 3460 | The Dark Knight | 2008 | 13 | 9 | 87 | Christopher Nolan | Christian Bale,Heath Ledger,Aaron Eckhart,Mich... | Action,Crime,Drama,Thriller | United States,United Kingdom | ... | Set within a year after the events of Batman B... | 152 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 3460 | 3461 | GoodFellas | 1990 | 16 | 8.7 | 96 | Martin Scorsese | Robert De Niro,Ray Liotta,Joe Pesci,Lorraine B... | Biography,Crime,Drama | United States | ... | Henry Hill might be a small time gangster, who... | 146 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 3462 | 3463 | Good Will Hunting | 1997 | 16 | 8.3 | 98 | Gus Van Sant | Matt Damon,Ben Affleck,Stellan Skarsgård,John ... | Drama,Romance | United States | ... | A touching tale of a wayward young man who str... | 126 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 3463 | 3464 | The Green Mile | 1999 | 16 | 8.6 | 78 | Frank Darabont | Tom Hanks,David Morse,Bonnie Hunt,Michael Clar... | Crime,Drama,Fantasy,Mystery | United States | ... | Death Row guards at a penitentiary, in the 193... | 189 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 3464 | 3465 | Batman Begins | 2005 | 13 | 8.2 | 84 | Christopher Nolan | Christian Bale,Michael Caine,Liam Neeson,Katie... | Action,Adventure | United States,United Kingdom | ... | When his parents are killed, billionaire playb... | 140 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16590 | 16591 | Red Nose Day | 2015 | NR | 6.7 | NA | Ryan Polito | Malin Akerman,Kristen Bell,Jack Black,Bono,Yve... | Comedy | United States | ... | NA | 120 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 16591 | 16592 | The Paley Center | 2000 | NR | 9.1 | NA | Brad Lachman | Margaret Cho,Raúl Esparza,Kelli Giddish,Marisk... | NA | United States | ... | NA | NA | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 16592 | 16593 | Summertime | 2016 | NR | 6.7 | NA | Catherine Corsini | Cécile de France,Izïa Higelin,Noémie Lvovsky,J... | Drama,Romance | France,Belgium | ... | NA | 105 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 16593 | 16594 | Bioterrorism: The Truth | 2016 | NR | 7.3 | NA | Leonard Horowitz | Leonard Horowitz | Documentary | United States | ... | Josh Rosenzweig, co-host of "Here with Josh & ... | 150 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
| 16594 | 16595 | Dick Clark's Primetime New Year's Rockin' Eve | 2013 | NR | 4.8 | NA | Dennis Rosenblatt | Ryan Seacrest,Jenny McCarthy,Jason Aldean,JT A... | Comedy,Music | United States | ... | An innovative look at the life of fictional Ma... | NA | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 1 |
783 rows × 21 columns
# Movies Available only on Prime Video
print('\nTotal ', df_prime_video_only_movies_ott['Title'].shape[0], ' Titles are available only on Prime Video\n')
df_prime_video_only_movies_ott
Total 11709 Titles are available only on Prime Video
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4314 | 4315 | It's a Wonderful Life | 1946 | 7 | 8.6 | 94 | Frank Capra | James Stewart,Donna Reed,Lionel Barrymore,Thom... | Drama,Family,Fantasy | United States | ... | George Bailey has spent his entire life giving... | 130 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4315 | 4316 | Downfall | 2004 | 16 | 8.2 | 90 | Oliver Hirschbiegel | Bruno Ganz,Alexandra Maria Lara,Corinna Harfou... | Biography,Drama,History,War | Germany,Austria,Italy | ... | In April of 1945, Germany stands at the brink ... | 156 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4316 | 4317 | Sunset Boulevard | 1950 | NR | 8.4 | 99 | Billy Wilder | William Holden,Gloria Swanson,Erich von Strohe... | Drama,Film-Noir | United States | ... | In Hollywood of the 50's, the obscure screenpl... | 110 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4317 | 4318 | Airplane! | 1980 | 7 | 7.7 | 97 | Jim Abrahams,David Zucker,Jerry Zucker | Kareem Abdul-Jabbar,Lloyd Bridges,Peter Graves... | Comedy | United States | ... | Drowning his sorrows after that botched missio... | 88 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4318 | 4319 | Some Like It Hot | 1959 | NR | 8.2 | 95 | Billy Wilder | Marilyn Monroe,Tony Curtis,Jack Lemmon,George ... | Comedy,Music,Romance | United States | ... | After two Chicago musicians, Joe and Jerry, wi... | 121 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16898 | 16899 | Heaven on Earth | 2004 | NR | 4.8 | NA | Kay Pollak | Axelle Axell,Björn Bengtsson,Eric Ericson,Nikl... | Comedy,Drama,Music,Romance | Sweden | ... | Take an exotic location, add a mystery and som... | 134 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 16899 | 16900 | Bigger Questions | 2017 | NR | 5 | NA | Ron James | Amit Goswami,Ron James,Charles Tart,William Ti... | Documentary | United States | ... | Babies of Car City are the Cars and Trucks' so... | 52 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 16900 | 16901 | My Battalion | 2014 | NR | 6.5 | NA | Dmitriy Meskhiev | Lesya Andreeva,Mariya Antonova,Mariya Aronova,... | Action,Drama,History,War | Russia | ... | Becky (Rachel McAdams) is a hard-working morni... | 120 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 16901 | 16902 | Morning Glory | 2013 | 13 | 6.5 | NA | Roger Michell | Rachel McAdams,Noah Bean,Jack Davidson,Vanessa... | Comedy,Drama,Romance | United States | ... | NA | 107 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 16902 | 16903 | Dream Town | 2015 | 18 | 6.2 | NA | Johannes Schaaf | Per Oscarsson,Rosemarie Fendel,Olimpia,Eva Mar... | Drama,Fantasy,Horror | West Germany | ... | The war is over and almost everyone has experi... | 124 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
11709 rows × 21 columns
# Movies Available only on Disney+
print('\nTotal ', df_disney_only_movies_ott['Title'].shape[0], ' Titles are available only on Disney+\n')
df_disney_only_movies_ott
Total 530 Titles are available only on Disney+
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | OTT Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15730 | 15731 | Star Wars: A New Hope | 1977 | 7 | 8.6 | 92 | George Lucas | Mark Hamill,Harrison Ford,Carrie Fisher,Peter ... | Action,Adventure,Fantasy,Sci-Fi | United States,United Kingdom | ... | From the largest elephant to the smallest shre... | 121 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 15731 | 15732 | Star Wars: The Empire Strikes Back | 1980 | 7 | 8.7 | 94 | Irvin Kershner | Mark Hamill,Harrison Ford,Carrie Fisher,Billy ... | Action,Adventure,Fantasy,Sci-Fi | United States,United Kingdom | ... | 30 years after the defeat of Darth Vader and t... | 124 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 15732 | 15733 | The Lion King | 1994 | 7 | 6.9 | 93 | Jon Favreau | Chiwetel Ejiofor,John Oliver,James Earl Jones,... | Animation,Adventure,Drama,Family,Musical | United States,United Kingdom,South Africa | ... | With many people fearing the actions of super ... | 118 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 15733 | 15734 | Toy Story | 1995 | 0 | 8.3 | 98 | John Lasseter | Tom Hanks,Tim Allen,Don Rickles,Jim Varney,Wal... | Animation,Adventure,Comedy,Family,Fantasy | United States | ... | Imprisoned on the other side of the universe, ... | 81 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 15734 | 15735 | Avengers: Endgame | 2019 | 13 | 8.4 | 94 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Evans,Mark Ruffalo,Chr... | Action,Adventure,Drama,Sci-Fi | United States | ... | An elderly man reads the book "The Princess Br... | 181 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16918 | 16919 | Pick of the Litter | 2019 | 7 | 7.5 | NA | Don Hardy,Dana Nachman | Diane Meer,Terry Blosser,Janet Gearheart,Sharo... | Documentary | United States | ... | Join National Geographic Explorer and photogra... | 80 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16919 | 16920 | The Lodge | 2016 | 16 | 6.1 | NA | Severin Fiala,Veronika Franz | Riley Keough,Jaeden Martell,Lia McHugh,Richard... | Drama,Horror,Thriller | United Kingdom,United States,Canada | ... | NA | 108 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16920 | 16921 | Spin and Marty | 1955 | 0 | 7.6 | NA | NA | Tim Considine,David Stollery,Roy Barcroft,Harr... | Family,Western | United States | ... | Each inspiring episode of Dog: Impossible foll... | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16921 | 16922 | Teacher's Pet | 2000 | 0 | 7.1 | NA | George Seaton | Clark Gable,Doris Day,Gig Young,Mamie Van Dore... | Comedy,Romance | United States | ... | NA | 120 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
| 16922 | 16923 | Paradise Islands | 2017 | 13 | NA | NA | NA | NA | Drama | United States | ... | NA | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 1 |
530 rows × 21 columns
ott_group_count = df_movies_ott.groupby('OTT Count')['Title'].count()
ott_group_movies = df_movies_ott.groupby('OTT Count')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
ott_group_data_movies = pd.concat([ott_group_count, ott_group_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
ott_group_data_movies = ott_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
ott_group_data_movies
| OTT Count | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 1 | 16290 | 3268 | 783 | 11709 | 530 |
| 1 | 2 | 624 | 362 | 268 | 589 | 29 |
| 2 | 3 | 9 | 7 | 9 | 9 | 2 |
# Title Group with Movies Counts - All Platforms Combined
ott_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
| OTT Count | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 1 | 16290 | 3268 | 783 | 11709 | 530 |
| 1 | 2 | 624 | 362 | 268 | 589 | 29 |
| 2 | 3 | 9 | 7 | 9 | 9 | 2 |
ott_group_data_movies.sort_values(by = 'OTT Count', ascending = False)
| OTT Count | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 2 | 3 | 9 | 7 | 9 | 9 | 2 |
| 1 | 2 | 624 | 362 | 268 | 589 | 29 |
| 0 | 1 | 16290 | 3268 | 783 | 11709 | 530 |
fig = px.bar(y = ott_group_data_movies['Movies Count'],
x = ott_group_data_movies['OTT Count'],
color = ott_group_data_movies['OTT Count'],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'OTT Count'},
title = 'Movies with Group Title : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
fig = px.pie(ott_group_data_movies,
names = ott_group_data_movies['OTT Count'],
values = ott_group_data_movies['Movies Count'],
color = ott_group_data_movies['Movies Count'],
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textinfo = 'percent+label',
title = 'Movies Count based on OTT Count')
fig.show()